class_docstr: This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer
  DeepSpeech and PyTorch. It supports both version 2 and version 3 of DeepSpeech models as released at
  https://github.com/SeanNaren/deepspeech.pytorch.
  
  | Paper link: https://arxiv.org/abs/1512.02595
docstr: Compute loss function and decoded output.
  
  :param masked_adv_input: The perturbed inputs.
  :param original_output: Target values of shape (nb_samples). Each sample in `original_output` is a string and
                          it may possess different lengths. A possible example of `original_output` could be:
                          `original_output = np.array(['SIXTY ONE', 'HELLO'])`.
  :param real_lengths: Real lengths of original sequences.
  :return: The loss and the decoded output.
docstr_tok: ["Compute","loss","function","and","decoded","output",".",":","param","masked_adv_input",":","The","perturbed","inputs",".",":","param","original_output",":","Target","values","of","shape","(","nb_samples",")",".","Each","sample","in","`","original_output","`","is","a","string","and","it","may","possess","different","lengths",".","A","possible","example","of","`","original_output","`","could","be",":","`","original_output","=","np.array","(","[","'SIXTY","ONE","'",",","'HELLO","'","]",")","`",".",":","param","real_lengths",":","Real","lengths","of","original","sequences",".",":","return",":","The","loss","and","the","decoded","output","."]
code: def compute_loss_and_decoded_output(
      self,
      masked_adv_input: "torch.Tensor",
      original_output: np.ndarray,
      **kwargs,
  ) -> Tuple["torch.Tensor", np.ndarray]:
      """
      Compute loss function and decoded output.
  
      :param masked_adv_input: The perturbed inputs.
      :param original_output: Target values of shape (nb_samples). Each sample in `original_output` is a string and
                              it may possess different lengths. A possible example of `original_output` could be:
                              `original_output = np.array(['SIXTY ONE', 'HELLO'])`.
      :param real_lengths: Real lengths of original sequences.
      :return: The loss and the decoded output.
      """
      # This estimator needs to have real lengths for loss computation
      real_lengths = kwargs.get("real_lengths")
      if real_lengths is None:  # pragma: no cover
          raise ValueError(
              "The PyTorchDeepSpeech estimator needs information about the real lengths of input sequences to "
              "compute loss and decoded output."
          )
  
      # Transform data into the model input space
      (
          inputs,
          targets,
          input_rates,
          target_sizes,
          batch_idx,
      ) = self._preprocess_transform_model_input(
          x=masked_adv_input.to(self.device),
          y=original_output,
          real_lengths=real_lengths,
      )
  
      # Compute real input sizes
      input_sizes = input_rates.mul_(inputs.size()[-1]).int()
  
      # Call to DeepSpeech model for prediction
      outputs, output_sizes, _ = self.model(
          inputs.to(self.device), input_sizes.to(self.device)
      )
      outputs_ = outputs.transpose(0, 1)
  
      if self._version == 2:
          outputs_ = outputs_.float()
      else:
          outputs_ = outputs_.log_softmax(-1)
  
      # Compute the loss
      loss = self.criterion(
          outputs_, targets, output_sizes, target_sizes
      ).to(self._device)
  
      # Average the loss by mini batch if version 2 of DeepSpeech is used
      if self._version == 2:
          loss = loss / inputs.size(0)
  
      # Compute transcription
      decoded_output, _ = self.decoder.decode(outputs, output_sizes)
      decoded_output = [do[0] for do in decoded_output]
      decoded_output = np.array(decoded_output)
  
      # Rearrange to the original order
      decoded_output_ = decoded_output.copy()
      decoded_output[batch_idx] = decoded_output_
  
      return loss, decoded_output
  
imports: import logging
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from pkg_resources import packaging
  import numpy
  from art import config
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin, PytorchSpeechRecognizerMixin
  from art.utils import get_file
label_desc: Use image node_id 5 for calling the PyTorchDeepSpeech obj's underlying member method code with example usage: obj.compute_loss_and_decoded_output(masked_adv_input, original_output) and returns: loss,  decoded_output